Can graph-based methods improve Arabic text summarization? This paper introduces a graph-based extractive Arabic text summarization (GEATS) technique. Employing word embedding and PageRank algorithms, the GEATS technique extracts key features and orders sentences to generate concise summaries. The method is specifically designed to handle the linguistic nuances of Arabic, a language with complex morphological linkages. Compared against state-of-the-art methods, the GEATS approach demonstrates superior performance, achieving an advantage of over 7.5% in F-measure values. These results highlight the effectiveness of graph-based techniques for Arabic text summarization and offer potential for wider application in natural language processing.
Published in the Arabian Journal for Science and Engineering, this article aligns with the journal's focus on technological advancements with relevance to the Arab world. Given the journal's scope which includes industrial engineering and computer science, the paper on Arabic text summarization fits well and contributes to its coverage of innovative engineering solutions.